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PurposeThis document analyses farmers' preferences and willingness to pay (CAP) for microcredit, in order to facilitate their access in rural areas.Design/methodology/approachData are based on a discrete choice experiment with 400 randomly selected farmers from 20 villages of the 7 Benin agricultural development hubs (ADHs). The preference choice modelling was performed using mixed logit (MXL) and latent class logit (LCL) models. Farmers' willingness to pay for each preferred attribute was estimated. The endogenous attribute attendance (EAA) model was also used to capture attribute non-attendance (ANA) phenomenon.FindingsThe results indicate that, on average, farmers prefer individual loans, low interest rates, in kind + cash loans, cash loans, disbursement before planting and loans with at least 10-month duration. These preferences vary according to farmers' classes. Farmers are willing to pay higher or lower interest rates depending on attribute importance. The estimate of the EAA model indicates that, when taking the ANA phenomenon into consideration, people will show stronger attitudes regarding WTP for important factors.Research limitations/implicationsBased on these results from Benin, microfinance institutions (MFIs) in developing countries can, based on the interest rates currently charged, attract more farmers as customers, reviewing the combination of the levels of the attributes associated with the nature of the loan, the type of loan (individual or collective), the disbursement period of funds, the waiting period of the loan and the loan duration. However, the study only considered production credit, ignoring equipment or investment credit.Practical implicationsThe document provides information on the key factors that can facilitate producers' access to MFI products and services.Social implicationsFacilitating small farmers' access to financial service will contribute to poverty reduction.Originality/valueThis research contributes to the knowledge of the attributes and attribute levels favoured by farmers when choosing financial products and the amounts they agree to pay for these attributes. The implementation of the results would facilitate small producers' access to financial services; thus contributing to poverty reduction.

Agricultural mechanization is on the rise in Africa. A widespread replacement of manual labor and animal traction will change the face of African agriculture. Despite this potentially transformative role, only a few studies have looked at the effects of mechanization empirically, mostly focusing on yields and labor alone. This is the first paper that explores perceived agronomic, environmental, and socioeconomic effects together, thereby revealing linkages and trade-offs, some of which have been hitherto unknown. Data were collected using a novel data collection method called “participatory impact diagrams” in four countries: Benin, Kenya, Nigeria, and Mali. In 129 gendered focus group discussions, 1330 respondents from 87 villages shared their perceptions on the positive and negative effects of agricultural mechanization, and developed causal impact chains. The results suggest that mechanization is likely to have more far-reaching agronomic, environmental, and socioeconomic consequences than commonly assumed. Most perceived effects were positive, suggesting that mechanization can help to reduce poverty and enhance food security but other effects were negative such as deforestation, soil erosion, land-use conflicts, and gender inequalities. Accompanying research and policy efforts, which reflect variations in local agro-ecological and socioeconomic conditions, are needed to ensure that mechanization contributes to an African agricultural transformation that is sustainable from a social, economic, and environmental perspective.

Fish farming is promoted in the Republic of Benin to meet the demands of fish consumption and increase exports to neighbouring countries. Targeting fish farm policy interventions to increase the efficiency of heterogeneous fish farming systems is a challenge. Farm type delineation allows for simplifying the diversity in fish farming systems. Multivariate statistical techniques combined with the Calinski and Harabasz pseudo F statistic and bootstrapping were applied to determine the realistic number of fish farm types. Four fish farm types were identified and characterised based on farm intensity, species diversification, and the management capacity of operators. Furthermore, profits and both labour and capital productivity increased continuously from the extensive fish farms to the semi‐intensive farms. They also vary widely within farm types across the country. These farm types may be supported by appropriate fish farming promotion policy interventions. Most importantly, training programs must be tailored to each farm type to strengthen the technical and managerial capacity of the fish farmers.

The aim of this study is to identify the attributes of storage structures sought by maize producers based on a choice experiment. The experimental processes took place in the maize production areas of northern and central Benin. The sample consisted of 365 maize farmers (80.55% male and 19.45% female) randomly selected from 40 villages. Data were collected and analyzed using a latent class logit model to study the heterogeneous preferences of the key attributes of storage structures. The results show that men and women are eager to change their current practices and to adopt new storage technologies. The study identifies four potential producer segments, including three large-farmer segments that have access to credit and are particularly attracted to structures related to metal silos. Of those three segments, two are also attracted to improved traditional silos. Another segment of poor farmers, who do not have access to credit, prefer to have a very efficient structure (loss rate of less than 5%) that is designed with local materials. This study suggests that knowledge of the heterogeneity of preferences, as well as the preferred attributes, is important for the development and dissemination of better technologies by agribusiness firms, institutions and policymakers.